# How to make a randomForest algorithm cost-sensitive?

Having used randomForest in R to produce a fairly successful classifier is there any way to emphasise sensitivity over specificity, for example, if the cost of missing a disease is much greater than diagnosing a false positive?

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Looks like the bst package in R will accept weights for false positive and false negatives. I'm going to leave the question open though, It might be useful to others. –  rosser Nov 25 '11 at 15:42
biomedcentral.com/content/pdf/1471-2105-10-S1-S22.pdf Using random forest for reliable classification and cost-sensitive learning for medical diagnosis –  rosser Nov 25 '11 at 16:01
Sure. I'll add that in case of randomForest, OOB votes are in votes element of the randomForest object; for prediction, one must use predict with type="votes" or type="prob". –  mbq Nov 25 '11 at 22:41